Overview

Dataset statistics

Number of variables18
Number of observations9626
Missing cells8650
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory144.0 B

Variable types

Categorical2
Numeric16

Alerts

name has a high cardinality: 9626 distinct values High cardinality
morningstar_category has a high cardinality: 119 distinct values High cardinality
ytdDaily is highly correlated with yr1 and 6 other fieldsHigh correlation
yr1 is highly correlated with ytdDaily and 6 other fieldsHigh correlation
yr3 is highly correlated with ytdDaily and 7 other fieldsHigh correlation
yr5 is highly correlated with ytdDaily and 6 other fieldsHigh correlation
yr10 is highly correlated with ytdDaily and 6 other fieldsHigh correlation
life_of_fund is highly correlated with ytdDaily and 6 other fieldsHigh correlation
net_expense_ratio is highly correlated with gross_expense_ratioHigh correlation
gross_expense_ratio is highly correlated with net_expense_ratioHigh correlation
risk is highly correlated with ytdDaily and 6 other fieldsHigh correlation
std_dev is highly correlated with ytdDaily and 6 other fieldsHigh correlation
sharpe_ratio_3_yr is highly correlated with yr3High correlation
ytdDaily is highly correlated with yr1 and 5 other fieldsHigh correlation
yr1 is highly correlated with ytdDaily and 5 other fieldsHigh correlation
yr3 is highly correlated with ytdDaily and 7 other fieldsHigh correlation
yr5 is highly correlated with ytdDaily and 8 other fieldsHigh correlation
yr10 is highly correlated with ytdDaily and 7 other fieldsHigh correlation
life_of_fund is highly correlated with yr3 and 3 other fieldsHigh correlation
morningstar_rating_overall is highly correlated with yr5 and 1 other fieldsHigh correlation
risk is highly correlated with ytdDaily and 5 other fieldsHigh correlation
std_dev is highly correlated with ytdDaily and 6 other fieldsHigh correlation
sharpe_ratio_3_yr is highly correlated with yr3 and 1 other fieldsHigh correlation
ytdDaily is highly correlated with yr1 and 4 other fieldsHigh correlation
yr1 is highly correlated with ytdDaily and 5 other fieldsHigh correlation
yr3 is highly correlated with yr1 and 5 other fieldsHigh correlation
yr5 is highly correlated with ytdDaily and 6 other fieldsHigh correlation
yr10 is highly correlated with ytdDaily and 6 other fieldsHigh correlation
life_of_fund is highly correlated with yr3 and 2 other fieldsHigh correlation
net_expense_ratio is highly correlated with gross_expense_ratioHigh correlation
gross_expense_ratio is highly correlated with net_expense_ratioHigh correlation
risk is highly correlated with ytdDaily and 5 other fieldsHigh correlation
std_dev is highly correlated with ytdDaily and 5 other fieldsHigh correlation
ytdDaily is highly correlated with yr1 and 5 other fieldsHigh correlation
yr1 is highly correlated with ytdDaily and 8 other fieldsHigh correlation
yr3 is highly correlated with ytdDaily and 7 other fieldsHigh correlation
yr5 is highly correlated with ytdDaily and 8 other fieldsHigh correlation
yr10 is highly correlated with ytdDaily and 8 other fieldsHigh correlation
life_of_fund is highly correlated with yr1 and 6 other fieldsHigh correlation
net_expense_ratio is highly correlated with sharpe_ratio_3_yrHigh correlation
morningstar_rating_overall is highly correlated with yr5 and 3 other fieldsHigh correlation
risk is highly correlated with ytdDaily and 8 other fieldsHigh correlation
std_dev is highly correlated with ytdDaily and 8 other fieldsHigh correlation
sharpe_ratio_3_yr is highly correlated with yr1 and 6 other fieldsHigh correlation
beta is highly correlated with r2High correlation
r2 is highly correlated with yr1 and 2 other fieldsHigh correlation
ytdDaily has 267 (2.8%) missing values Missing
yr1 has 106 (1.1%) missing values Missing
yr3 has 422 (4.4%) missing values Missing
yr5 has 704 (7.3%) missing values Missing
yr10 has 2343 (24.3%) missing values Missing
morningstar_rating_overall has 524 (5.4%) missing values Missing
std_dev has 472 (4.9%) missing values Missing
sharpe_ratio_3_yr has 472 (4.9%) missing values Missing
beta has 1437 (14.9%) missing values Missing
r2 has 1437 (14.9%) missing values Missing
last_dividend has 448 (4.7%) missing values Missing
last_dividend is highly skewed (γ1 = 37.00553154) Skewed
name is uniformly distributed Uniform
name has unique values Unique
r2 has 119 (1.2%) zeros Zeros
minimum_investment has 467 (4.9%) zeros Zeros
last_dividend has 517 (5.4%) zeros Zeros

Reproduction

Analysis started2021-11-10 17:53:37.783248
Analysis finished2021-11-10 17:54:16.283880
Duration38.5 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct9626
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size75.3 KiB
BlackRock High Yield Bond Portfolio Investor C Shares (BHYCX)
 
1
BlackRock Advantage Large Cap Value Fund Institutional Shares (MALVX)
 
1
Catalyst/Lyons Tactical Allocation Fund Class I (CLTIX)
 
1
BlackRock Advantage International Fund Investor C Shares (BROCX)
 
1
TIAA-CREF S&P 500 Index Fund Institutional Class (TISPX)
 
1
Other values (9621)
9621 

Length

Max length103
Median length52
Mean length53.14949096
Min length18

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9626 ?
Unique (%)100.0%

Sample

1st rowBaron Partners Fund Institutional Shares (BPTIX)
2nd rowBaron Partners Fund Retail Shares (BPTRX)
3rd rowMorgan Stanley Institutional Fund, Inc. Inception Portfolio Class I (MSSGX)
4th rowMorgan Stanley Institutional Fund, Inc. Inception Portfolio Class A (MSSMX)
5th rowMorgan Stanley Institutional Fund, Inc. Inception Portfolio Class C (MSCOX)

Common Values

ValueCountFrequency (%)
BlackRock High Yield Bond Portfolio Investor C Shares (BHYCX)1
 
< 0.1%
BlackRock Advantage Large Cap Value Fund Institutional Shares (MALVX)1
 
< 0.1%
Catalyst/Lyons Tactical Allocation Fund Class I (CLTIX)1
 
< 0.1%
BlackRock Advantage International Fund Investor C Shares (BROCX)1
 
< 0.1%
TIAA-CREF S&P 500 Index Fund Institutional Class (TISPX)1
 
< 0.1%
AlphaCentric LifeSci Healthcare Fund Class I (LYFIX)1
 
< 0.1%
USAA Intermediate-Term Bond Fund Class A (UITBX)1
 
< 0.1%
Thornburg Small/Mid Cap Growth Fund Class C (TCGCX)1
 
< 0.1%
Wasatch International Select Fund Investor Class Shares (WAISX)1
 
< 0.1%
Putnam Multi-Asset Absolute Return Fund Class C (PDMCX)1
 
< 0.1%
Other values (9616)9616
99.9%

Length

2021-11-10T12:54:16.394324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fund8862
 
11.5%
class7796
 
10.1%
a2620
 
3.4%
c1808
 
2.4%
income1303
 
1.7%
i1259
 
1.6%
institutional1251
 
1.6%
shares1098
 
1.4%
bond1074
 
1.4%
growth960
 
1.2%
Other values (11318)48901
63.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

morningstar_category
Categorical

HIGH CARDINALITY

Distinct119
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size75.3 KiB
Large Growth
 
515
Large Blend
 
503
Large Value
 
465
High Yield Bond
 
283
Diversified Emerging Mkts
 
280
Other values (114)
7580 

Length

Max length36
Median length16
Mean length17.23332641
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowLarge Growth
2nd rowLarge Growth
3rd rowSmall Growth
4th rowSmall Growth
5th rowSmall Growth

Common Values

ValueCountFrequency (%)
Large Growth515
 
5.4%
Large Blend503
 
5.2%
Large Value465
 
4.8%
High Yield Bond283
 
2.9%
Diversified Emerging Mkts280
 
2.9%
Allocation--50% to 70% Equity250
 
2.6%
Mid-Cap Growth245
 
2.5%
Small Blend242
 
2.5%
Small Growth231
 
2.4%
Foreign Large Blend230
 
2.4%
Other values (109)6382
66.3%

Length

2021-11-10T12:54:16.546743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
large1970
 
8.4%
bond1621
 
6.9%
growth1318
 
5.6%
blend1290
 
5.5%
value999
 
4.2%
muni935
 
4.0%
equity903
 
3.8%
world670
 
2.8%
small637
 
2.7%
to594
 
2.5%
Other values (130)12585
53.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ytdDaily
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3731
Distinct (%)39.9%
Missing267
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean12.84168821
Minimum-22.09
Maximum83.85
Zeros8
Zeros (%)0.1%
Negative1240
Negative (%)12.9%
Memory size75.3 KiB
2021-11-10T12:54:16.673688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-22.09
5-th percentile-1.621
Q11.89
median11.97
Q322.07
95-th percentile32.341
Maximum83.85
Range105.94
Interquartile range (IQR)20.18

Descriptive statistics

Standard deviation12.13765285
Coefficient of variation (CV)0.945175794
Kurtosis0.5911216272
Mean12.84168821
Median Absolute Deviation (MAD)10.09
Skewness0.6123101071
Sum120185.36
Variance147.3226168
MonotonicityNot monotonic
2021-11-10T12:54:16.801446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0140
 
0.4%
0.0215
 
0.2%
-0.4313
 
0.1%
0.0613
 
0.1%
-0.0413
 
0.1%
1.0613
 
0.1%
0.2212
 
0.1%
0.5412
 
0.1%
-0.5111
 
0.1%
0.1111
 
0.1%
Other values (3721)9206
95.6%
(Missing)267
 
2.8%
ValueCountFrequency (%)
-22.091
< 0.1%
-21.561
< 0.1%
-21.411
< 0.1%
-21.21
< 0.1%
-21.091
< 0.1%
-20.691
< 0.1%
-20.581
< 0.1%
-20.431
< 0.1%
-20.071
< 0.1%
-19.381
< 0.1%
ValueCountFrequency (%)
83.851
< 0.1%
83.411
< 0.1%
78.771
< 0.1%
76.411
< 0.1%
72.261
< 0.1%
71.761
< 0.1%
71.681
< 0.1%
70.841
< 0.1%
66.061
< 0.1%
65.461
< 0.1%

yr1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4844
Distinct (%)50.9%
Missing106
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean25.53231513
Minimum-37.66
Maximum146.46
Zeros4
Zeros (%)< 0.1%
Negative517
Negative (%)5.4%
Memory size75.3 KiB
2021-11-10T12:54:16.945561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-37.66
5-th percentile-0.11
Q16.01
median26.135
Q340.18
95-th percentile57.4205
Maximum146.46
Range184.12
Interquartile range (IQR)34.17

Descriptive statistics

Standard deviation20.38165392
Coefficient of variation (CV)0.7982689317
Kurtosis0.6369689208
Mean25.53231513
Median Absolute Deviation (MAD)16.835
Skewness0.5786136233
Sum243067.64
Variance415.4118164
MonotonicityNot monotonic
2021-11-10T12:54:17.087793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0132
 
0.3%
1.0914
 
0.1%
0.6710
 
0.1%
2.339
 
0.1%
0.079
 
0.1%
0.619
 
0.1%
1.319
 
0.1%
1.888
 
0.1%
0.148
 
0.1%
-0.328
 
0.1%
Other values (4834)9404
97.7%
(Missing)106
 
1.1%
ValueCountFrequency (%)
-37.661
< 0.1%
-30.361
< 0.1%
-29.831
< 0.1%
-29.691
< 0.1%
-29.611
< 0.1%
-29.081
< 0.1%
-28.91
< 0.1%
-23.031
< 0.1%
-19.651
< 0.1%
-15.941
< 0.1%
ValueCountFrequency (%)
146.461
< 0.1%
145.641
< 0.1%
127.921
< 0.1%
127.461
< 0.1%
126.641
< 0.1%
126.571
< 0.1%
125.161
< 0.1%
123.451
< 0.1%
122.911
< 0.1%
122.871
< 0.1%

yr3
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2942
Distinct (%)32.0%
Missing422
Missing (%)4.4%
Infinite0
Infinite (%)0.0%
Mean12.17078879
Minimum-29.38
Maximum65.56
Zeros1
Zeros (%)< 0.1%
Negative135
Negative (%)1.4%
Memory size75.3 KiB
2021-11-10T12:54:17.240838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-29.38
5-th percentile2.0915
Q15.46
median11.265
Q316.95
95-th percentile27.4785
Maximum65.56
Range94.94
Interquartile range (IQR)11.49

Descriptive statistics

Standard deviation8.276279943
Coefficient of variation (CV)0.6800117961
Kurtosis1.186144776
Mean12.17078879
Median Absolute Deviation (MAD)5.765
Skewness0.758643739
Sum112019.94
Variance68.4968097
MonotonicityNot monotonic
2021-11-10T12:54:17.389044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1816
 
0.2%
4.6916
 
0.2%
4.8114
 
0.1%
4.2914
 
0.1%
5.7514
 
0.1%
5.513
 
0.1%
17.5713
 
0.1%
4.7213
 
0.1%
4.4513
 
0.1%
3.8713
 
0.1%
Other values (2932)9065
94.2%
(Missing)422
 
4.4%
ValueCountFrequency (%)
-29.381
< 0.1%
-20.111
< 0.1%
-19.511
< 0.1%
-19.281
< 0.1%
-19.271
< 0.1%
-18.631
< 0.1%
-18.431
< 0.1%
-17.351
< 0.1%
-16.741
< 0.1%
-16.691
< 0.1%
ValueCountFrequency (%)
65.561
< 0.1%
65.131
< 0.1%
55.111
< 0.1%
54.71
< 0.1%
53.461
< 0.1%
51.071
< 0.1%
50.651
< 0.1%
50.471
< 0.1%
49.751
< 0.1%
49.551
< 0.1%

yr5
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2567
Distinct (%)28.8%
Missing704
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean9.872851379
Minimum-23.43
Maximum47.54
Zeros0
Zeros (%)0.0%
Negative121
Negative (%)1.3%
Memory size75.3 KiB
2021-11-10T12:54:17.516744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-23.43
5-th percentile1.45
Q13.74
median9.12
Q314.16
95-th percentile23.3995
Maximum47.54
Range70.97
Interquartile range (IQR)10.42

Descriptive statistics

Standard deviation7.080595292
Coefficient of variation (CV)0.7171783531
Kurtosis0.5194668561
Mean9.872851379
Median Absolute Deviation (MAD)5.26
Skewness0.6910403347
Sum88085.58
Variance50.13482969
MonotonicityNot monotonic
2021-11-10T12:54:17.653446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5120
 
0.2%
1.9118
 
0.2%
2.0917
 
0.2%
3.2316
 
0.2%
3.2915
 
0.2%
2.9615
 
0.2%
2.8215
 
0.2%
3.7615
 
0.2%
3.3414
 
0.1%
3.5414
 
0.1%
Other values (2557)8763
91.0%
(Missing)704
 
7.3%
ValueCountFrequency (%)
-23.431
< 0.1%
-18.81
< 0.1%
-18.191
< 0.1%
-17.971
< 0.1%
-16.051
< 0.1%
-15.511
< 0.1%
-15.41
< 0.1%
-15.231
< 0.1%
-14.881
< 0.1%
-14.871
< 0.1%
ValueCountFrequency (%)
47.541
< 0.1%
47.151
< 0.1%
40.231
< 0.1%
39.831
< 0.1%
38.471
< 0.1%
38.371
< 0.1%
38.091
< 0.1%
37.981
< 0.1%
37.871
< 0.1%
37.71
< 0.1%

yr10
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2059
Distinct (%)28.3%
Missing2343
Missing (%)24.3%
Infinite0
Infinite (%)0.0%
Mean8.45523548
Minimum-19.02
Maximum29.02
Zeros0
Zeros (%)0.0%
Negative159
Negative (%)1.7%
Memory size75.3 KiB
2021-11-10T12:54:17.788266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-19.02
5-th percentile1.101
Q13.76
median8.18
Q312.53
95-th percentile17.53
Maximum29.02
Range48.04
Interquartile range (IQR)8.77

Descriptive statistics

Standard deviation5.485702573
Coefficient of variation (CV)0.648793589
Kurtosis-0.2466504382
Mean8.45523548
Median Absolute Deviation (MAD)4.4
Skewness0.1367624069
Sum61579.48
Variance30.09293272
MonotonicityNot monotonic
2021-11-10T12:54:17.925136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.1715
 
0.2%
3.2115
 
0.2%
3.1114
 
0.1%
2.5813
 
0.1%
2.5313
 
0.1%
11.1812
 
0.1%
11.8312
 
0.1%
4.4612
 
0.1%
9.3712
 
0.1%
3.5212
 
0.1%
Other values (2049)7153
74.3%
(Missing)2343
 
24.3%
ValueCountFrequency (%)
-19.021
< 0.1%
-17.21
< 0.1%
-16.581
< 0.1%
-16.361
< 0.1%
-13.981
< 0.1%
-13.331
< 0.1%
-13.281
< 0.1%
-13.141
< 0.1%
-12.721
< 0.1%
-12.621
< 0.1%
ValueCountFrequency (%)
29.021
< 0.1%
28.681
< 0.1%
25.581
< 0.1%
25.531
< 0.1%
24.991
< 0.1%
24.671
< 0.1%
24.621
< 0.1%
24.431
< 0.1%
24.341
< 0.1%
24.151
< 0.1%

life_of_fund
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1933
Distinct (%)20.1%
Missing13
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7.796145844
Minimum-11.74
Maximum82.45
Zeros5
Zeros (%)0.1%
Negative149
Negative (%)1.5%
Memory size75.3 KiB
2021-11-10T12:54:18.065526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-11.74
5-th percentile1.856
Q14.65
median7.08
Q310.17
95-th percentile15.014
Maximum82.45
Range94.19
Interquartile range (IQR)5.52

Descriptive statistics

Standard deviation5.245337014
Coefficient of variation (CV)0.6728115558
Kurtosis23.82487388
Mean7.796145844
Median Absolute Deviation (MAD)2.7
Skewness3.142684229
Sum74944.35
Variance27.51356039
MonotonicityNot monotonic
2021-11-10T12:54:18.212930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.2621
 
0.2%
4.9420
 
0.2%
4.8520
 
0.2%
4.819
 
0.2%
5.5119
 
0.2%
5.5319
 
0.2%
5.1619
 
0.2%
5.3418
 
0.2%
4.6618
 
0.2%
5.118
 
0.2%
Other values (1923)9422
97.9%
ValueCountFrequency (%)
-11.741
< 0.1%
-11.141
< 0.1%
-9.081
< 0.1%
-8.61
< 0.1%
-8.371
< 0.1%
-7.751
< 0.1%
-7.671
< 0.1%
-7.551
< 0.1%
-7.21
< 0.1%
-7.041
< 0.1%
ValueCountFrequency (%)
82.451
< 0.1%
67.121
< 0.1%
64.911
< 0.1%
64.532
< 0.1%
63.041
< 0.1%
60.481
< 0.1%
57.561
< 0.1%
57.11
< 0.1%
55.281
< 0.1%
542
< 0.1%

net_expense_ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct370
Distinct (%)3.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.154080519
Minimum0
Maximum5.25
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size75.3 KiB
2021-11-10T12:54:18.363525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q10.76
median1.05
Q31.49
95-th percentile2.14
Maximum5.25
Range5.25
Interquartile range (IQR)0.73

Descriptive statistics

Standard deviation0.5709649079
Coefficient of variation (CV)0.4947357643
Kurtosis2.255802745
Mean1.154080519
Median Absolute Deviation (MAD)0.34
Skewness1.047062756
Sum11108.025
Variance0.3260009261
MonotonicityNot monotonic
2021-11-10T12:54:18.509552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1167
 
1.7%
0.75151
 
1.6%
0.9151
 
1.6%
0.95145
 
1.5%
0.85142
 
1.5%
0.7134
 
1.4%
1.05125
 
1.3%
1.15116
 
1.2%
0.8111
 
1.2%
0.99110
 
1.1%
Other values (360)8273
85.9%
ValueCountFrequency (%)
04
 
< 0.1%
0.0152
 
< 0.1%
0.021
 
< 0.1%
0.0253
 
< 0.1%
0.035
 
0.1%
0.0356
 
0.1%
0.045
 
0.1%
0.0518
0.2%
0.0551
 
< 0.1%
0.067
 
0.1%
ValueCountFrequency (%)
5.251
< 0.1%
4.781
< 0.1%
4.741
< 0.1%
4.531
< 0.1%
4.351
< 0.1%
4.231
< 0.1%
4.191
< 0.1%
4.11
< 0.1%
4.031
< 0.1%
3.991
< 0.1%

gross_expense_ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct546
Distinct (%)5.7%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.412056837
Minimum0
Maximum46.99
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size75.3 KiB
2021-11-10T12:54:18.629361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.46
Q10.85
median1.19
Q31.7
95-th percentile2.7
Maximum46.99
Range46.99
Interquartile range (IQR)0.85

Descriptive statistics

Standard deviation1.33312706
Coefficient of variation (CV)0.9441029743
Kurtosis263.2903771
Mean1.412056837
Median Absolute Deviation (MAD)0.4
Skewness12.10816825
Sum13589.635
Variance1.777227757
MonotonicityNot monotonic
2021-11-10T12:54:18.780809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0592
 
1.0%
1.0490
 
0.9%
0.9590
 
0.9%
1.188
 
0.9%
0.9287
 
0.9%
1.0283
 
0.9%
0.9882
 
0.9%
0.9682
 
0.9%
0.9980
 
0.8%
0.9479
 
0.8%
Other values (536)8771
91.1%
ValueCountFrequency (%)
04
 
< 0.1%
0.0152
 
< 0.1%
0.021
 
< 0.1%
0.0253
 
< 0.1%
0.035
 
0.1%
0.0356
 
0.1%
0.045
 
0.1%
0.0518
0.2%
0.0551
 
< 0.1%
0.067
 
0.1%
ValueCountFrequency (%)
46.991
< 0.1%
31.891
< 0.1%
29.541
< 0.1%
25.071
< 0.1%
24.971
< 0.1%
24.821
< 0.1%
24.721
< 0.1%
20.931
< 0.1%
19.861
< 0.1%
19.111
< 0.1%

morningstar_rating_overall
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct94
Distinct (%)1.0%
Missing524
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean464.1526038
Minimum10
Maximum1250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.3 KiB
2021-11-10T12:54:18.909495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile63
Q1186
median361
Q3631
95-th percentile1250
Maximum1250
Range1240
Interquartile range (IQR)445

Descriptive statistics

Standard deviation362.4563677
Coefficient of variation (CV)0.7808991369
Kurtosis-0.2542350248
Mean464.1526038
Median Absolute Deviation (MAD)209
Skewness0.9434778562
Sum4224717
Variance131374.6185
MonotonicityNot monotonic
2021-11-10T12:54:19.037604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1137504
 
5.2%
1250473
 
4.9%
1146461
 
4.8%
631276
 
2.9%
711266
 
2.8%
550236
 
2.5%
669235
 
2.4%
601234
 
2.4%
574226
 
2.3%
687223
 
2.3%
Other values (84)5968
62.0%
(Missing)524
 
5.4%
ValueCountFrequency (%)
106
 
0.1%
163
 
< 0.1%
2010
 
0.1%
248
 
0.1%
2726
0.3%
309
 
0.1%
316
 
0.1%
3212
 
0.1%
3332
0.3%
3925
0.3%
ValueCountFrequency (%)
1250473
4.9%
1146461
4.8%
1137504
5.2%
711266
2.8%
687223
2.3%
669235
2.4%
631276
2.9%
601234
2.4%
574226
2.3%
570217
2.3%

risk
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.271556202
Minimum0
Maximum9
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size75.3 KiB
2021-11-10T12:54:19.154233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q36
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.665108774
Coefficient of variation (CV)0.3158666455
Kurtosis-0.5915592103
Mean5.271556202
Median Absolute Deviation (MAD)1
Skewness-0.3630102343
Sum50744
Variance2.77258723
MonotonicityNot monotonic
2021-11-10T12:54:19.276484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
63293
34.2%
31330
13.8%
71314
 
13.7%
51248
 
13.0%
41153
 
12.0%
8685
 
7.1%
2508
 
5.3%
946
 
0.5%
136
 
0.4%
013
 
0.1%
ValueCountFrequency (%)
013
 
0.1%
136
 
0.4%
2508
 
5.3%
31330
13.8%
41153
 
12.0%
51248
 
13.0%
63293
34.2%
71314
 
13.7%
8685
 
7.1%
946
 
0.5%
ValueCountFrequency (%)
946
 
0.5%
8685
 
7.1%
71314
 
13.7%
63293
34.2%
51248
 
13.0%
41153
 
12.0%
31330
13.8%
2508
 
5.3%
136
 
0.4%
013
 
0.1%

std_dev
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2722
Distinct (%)29.7%
Missing472
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean14.54715425
Minimum0.18
Maximum59.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.3 KiB
2021-11-10T12:54:19.397787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.18
5-th percentile2.7765
Q17.1925
median16.08
Q319.87
95-th percentile26.55
Maximum59.74
Range59.56
Interquartile range (IQR)12.6775

Descriptive statistics

Standard deviation8.134948259
Coefficient of variation (CV)0.5592123462
Kurtosis0.4850435193
Mean14.54715425
Median Absolute Deviation (MAD)5.92
Skewness0.3865247409
Sum133164.65
Variance66.17738317
MonotonicityNot monotonic
2021-11-10T12:54:19.522722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.4820
 
0.2%
18.4117
 
0.2%
18.4316
 
0.2%
3.6915
 
0.2%
18.8115
 
0.2%
415
 
0.2%
18.8914
 
0.1%
18.5614
 
0.1%
19.5113
 
0.1%
18.8813
 
0.1%
Other values (2712)9002
93.5%
(Missing)472
 
4.9%
ValueCountFrequency (%)
0.181
< 0.1%
0.211
< 0.1%
0.221
< 0.1%
0.312
< 0.1%
0.371
< 0.1%
0.441
< 0.1%
0.481
< 0.1%
0.491
< 0.1%
0.531
< 0.1%
0.542
< 0.1%
ValueCountFrequency (%)
59.741
< 0.1%
59.361
< 0.1%
59.261
< 0.1%
59.211
< 0.1%
56.461
< 0.1%
56.441
< 0.1%
56.421
< 0.1%
56.41
< 0.1%
52.841
< 0.1%
52.741
< 0.1%

sharpe_ratio_3_yr
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct293
Distinct (%)3.2%
Missing472
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean0.7845488311
Minimum-2.45
Maximum2.61
Zeros8
Zeros (%)0.1%
Negative213
Negative (%)2.2%
Memory size75.3 KiB
2021-11-10T12:54:19.653467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.45
5-th percentile0.19
Q10.55
median0.78
Q31.03
95-th percentile1.42
Maximum2.61
Range5.06
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.3915775838
Coefficient of variation (CV)0.4991118057
Kurtosis2.825971323
Mean0.7845488311
Median Absolute Deviation (MAD)0.24
Skewness-0.4341838013
Sum7181.76
Variance0.1533330041
MonotonicityNot monotonic
2021-11-10T12:54:19.775306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.64124
 
1.3%
0.84118
 
1.2%
0.62118
 
1.2%
0.66118
 
1.2%
0.9116
 
1.2%
0.75111
 
1.2%
0.76109
 
1.1%
0.85108
 
1.1%
0.65108
 
1.1%
0.67107
 
1.1%
Other values (283)8017
83.3%
(Missing)472
 
4.9%
ValueCountFrequency (%)
-2.451
< 0.1%
-1.792
< 0.1%
-1.51
< 0.1%
-1.451
< 0.1%
-1.441
< 0.1%
-1.341
< 0.1%
-1.321
< 0.1%
-1.312
< 0.1%
-1.271
< 0.1%
-1.262
< 0.1%
ValueCountFrequency (%)
2.611
< 0.1%
2.591
< 0.1%
2.551
< 0.1%
2.451
< 0.1%
2.241
< 0.1%
2.171
< 0.1%
2.141
< 0.1%
2.122
< 0.1%
2.112
< 0.1%
2.091
< 0.1%

beta
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct436
Distinct (%)5.3%
Missing1437
Missing (%)14.9%
Infinite0
Infinite (%)0.0%
Mean0.7881145439
Minimum-21
Maximum8.75
Zeros8
Zeros (%)0.1%
Negative292
Negative (%)3.0%
Memory size75.3 KiB
2021-11-10T12:54:19.906697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-21
5-th percentile0.24
Q10.82
median0.96
Q31.05
95-th percentile1.29
Maximum8.75
Range29.75
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation1.218248074
Coefficient of variation (CV)1.545775399
Kurtosis104.8637156
Mean0.7881145439
Median Absolute Deviation (MAD)0.11
Skewness-8.889376219
Sum6453.87
Variance1.484128369
MonotonicityNot monotonic
2021-11-10T12:54:20.035823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1380
 
3.9%
0.99269
 
2.8%
0.97262
 
2.7%
1.01245
 
2.5%
1.02237
 
2.5%
0.92225
 
2.3%
0.98222
 
2.3%
0.95213
 
2.2%
0.96213
 
2.2%
1.03207
 
2.2%
Other values (426)5716
59.4%
(Missing)1437
 
14.9%
ValueCountFrequency (%)
-212
< 0.1%
-20.981
< 0.1%
-16.711
< 0.1%
-16.581
< 0.1%
-14.81
< 0.1%
-14.681
< 0.1%
-14.61
< 0.1%
-14.571
< 0.1%
-14.561
< 0.1%
-14.551
< 0.1%
ValueCountFrequency (%)
8.751
< 0.1%
8.731
< 0.1%
5.051
< 0.1%
5.031
< 0.1%
4.491
< 0.1%
42
< 0.1%
3.961
< 0.1%
3.881
< 0.1%
3.861
< 0.1%
3.581
< 0.1%

r2
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct101
Distinct (%)1.2%
Missing1437
Missing (%)14.9%
Infinite0
Infinite (%)0.0%
Mean0.80588106
Minimum0
Maximum1
Zeros119
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size75.3 KiB
2021-11-10T12:54:20.167315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.81
median0.93
Q30.97
95-th percentile0.99
Maximum1
Range1
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation0.2823115313
Coefficient of variation (CV)0.350314141
Kurtosis2.202619972
Mean0.80588106
Median Absolute Deviation (MAD)0.05
Skewness-1.890071554
Sum6599.36
Variance0.0796998007
MonotonicityNot monotonic
2021-11-10T12:54:20.303317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.98788
 
8.2%
0.97711
 
7.4%
0.99693
 
7.2%
0.96649
 
6.7%
0.95568
 
5.9%
0.94466
 
4.8%
0.93304
 
3.2%
0.92302
 
3.1%
0.91262
 
2.7%
0.89194
 
2.0%
Other values (91)3252
33.8%
(Missing)1437
14.9%
ValueCountFrequency (%)
0119
1.2%
0.0173
0.8%
0.0274
0.8%
0.0361
0.6%
0.0463
0.7%
0.0537
 
0.4%
0.0638
 
0.4%
0.0740
 
0.4%
0.0831
 
0.3%
0.0927
 
0.3%
ValueCountFrequency (%)
1193
 
2.0%
0.99693
7.2%
0.98788
8.2%
0.97711
7.4%
0.96649
6.7%
0.95568
5.9%
0.94466
4.8%
0.93304
 
3.2%
0.92302
 
3.1%
0.91262
 
2.7%

minimum_investment
Real number (ℝ≥0)

ZEROS

Distinct28
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean245593.3602
Minimum0
Maximum9999999.99
Zeros467
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size75.3 KiB
2021-11-10T12:54:20.447025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile250
Q12500
median2500
Q35000
95-th percentile1000000
Maximum9999999.99
Range9999999.99
Interquartile range (IQR)2500

Descriptive statistics

Standard deviation838146.2083
Coefficient of variation (CV)3.412739691
Kurtosis42.54593872
Mean245593.3602
Median Absolute Deviation (MAD)1000
Skewness5.780646942
Sum2363590499
Variance7.024890664 × 1011
MonotonicityNot monotonic
2021-11-10T12:54:21.178178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
25004548
47.2%
10001331
 
13.8%
1000000802
 
8.3%
100000510
 
5.3%
0467
 
4.9%
250294
 
3.1%
5000285
 
3.0%
250000211
 
2.2%
3000190
 
2.0%
10000178
 
1.8%
Other values (18)808
 
8.4%
ValueCountFrequency (%)
0467
 
4.9%
250294
 
3.1%
50027
 
0.3%
10001331
 
13.8%
150098
 
1.0%
200050
 
0.5%
25004548
47.2%
3000190
 
2.0%
35001
 
< 0.1%
40003
 
< 0.1%
ValueCountFrequency (%)
9999999.9914
 
0.1%
5000000169
 
1.8%
300000014
 
0.1%
250000019
 
0.2%
2000000156
 
1.6%
15000001
 
< 0.1%
1000000802
8.3%
50000096
 
1.0%
250000211
 
2.2%
100000510
5.3%

last_dividend
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct6627
Distinct (%)72.2%
Missing448
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean0.1165819948
Minimum0
Maximum24.01277074
Zeros517
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size75.3 KiB
2021-11-10T12:54:21.340081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01388297925
median0.03448
Q30.118995
95-th percentile0.38383
Maximum24.01277074
Range24.01277074
Interquartile range (IQR)0.1051120207

Descriptive statistics

Standard deviation0.4992803289
Coefficient of variation (CV)4.282653852
Kurtosis1722.02442
Mean0.1165819948
Median Absolute Deviation (MAD)0.02918
Skewness37.00553154
Sum1069.989548
Variance0.2492808469
MonotonicityNot monotonic
2021-11-10T12:54:21.485058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0517
 
5.4%
8.494 × 10-635
 
0.4%
0.0125
 
0.3%
0.0218
 
0.2%
0.00118
 
0.2%
0.117
 
0.2%
0.01815
 
0.2%
0.00614
 
0.1%
0.01514
 
0.1%
0.02213
 
0.1%
Other values (6617)8492
88.2%
(Missing)448
 
4.7%
ValueCountFrequency (%)
0517
5.4%
3 × 10-91
 
< 0.1%
1.6 × 10-81
 
< 0.1%
2.1 × 10-81
 
< 0.1%
3.2 × 10-81
 
< 0.1%
4.8 × 10-81
 
< 0.1%
1.4 × 10-71
 
< 0.1%
3.1 × 10-71
 
< 0.1%
7.15 × 10-71
 
< 0.1%
1.097 × 10-61
 
< 0.1%
ValueCountFrequency (%)
24.012770743
< 0.1%
5.4487798353
< 0.1%
4.7139381
 
< 0.1%
3.96571
 
< 0.1%
3.61261
 
< 0.1%
3.54471
 
< 0.1%
3.4800470823
< 0.1%
3.23651
 
< 0.1%
3.14581
 
< 0.1%
3.03081
 
< 0.1%

Interactions

2021-11-10T12:54:12.853107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:41.063959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:43.091959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:45.183573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:47.489516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:49.621517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:51.683656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-10T12:54:14.503841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:42.719451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:44.789490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:47.113366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:49.220261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:51.300501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:53.353086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:55.431613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:57.431215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:59.526342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:01.540596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:03.974282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:06.105837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:08.218975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:10.333227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:12.454087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:14.633079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:42.838508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:44.916883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:47.238392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:49.349544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:51.426468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:53.488813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:55.552557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:57.565344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:59.657530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:01.662335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:04.116840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:06.249853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:08.355915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:10.488474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:12.586015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:14.759196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:42.962286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:45.056332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:47.367844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:49.486803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:51.558990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:53.616509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:55.681367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:57.694759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:53:59.789324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:01.788983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:04.262424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:06.388173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:08.493245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:10.618560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-10T12:54:12.719797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-11-10T12:54:21.629695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-10T12:54:21.910173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-10T12:54:22.168155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-10T12:54:22.414418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-10T12:54:15.012600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-10T12:54:15.372555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-11-10T12:54:15.730954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-11-10T12:54:16.120598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

namemorningstar_categoryytdDailyyr1yr3yr5yr10life_of_fundnet_expense_ratiogross_expense_ratiomorningstar_rating_overallriskstd_devsharpe_ratio_3_yrbetar2minimum_investmentlast_dividend
0Baron Partners Fund Institutional Shares (BPTIX)Large Growth44.60110.2765.5647.5429.0227.021.301.301137.0640.411.601.510.631000000.00.2224
1Baron Partners Fund Retail Shares (BPTRX)Large Growth44.28109.7265.1347.1528.6820.451.561.561137.0640.391.591.510.632500.00.1243
2Morgan Stanley Institutional Fund, Inc. Inception Portfolio Class I (MSSGX)Small Growth20.0581.1555.1138.3722.3714.141.001.19574.0740.441.341.400.715000000.00.0000
3Morgan Stanley Institutional Fund, Inc. Inception Portfolio Class A (MSSMX)Small Growth19.7580.6754.7037.9822.0113.831.351.45574.0740.481.321.400.712500.00.0000
4Morgan Stanley Institutional Fund, Inc. Inception Portfolio Class C (MSCOX)Small GrowthNaN79.3953.4636.9021.1113.002.102.27574.0740.421.301.400.712500.0NaN
5Baron Focused Growth Fund Institutional Shares (BFGIX)Mid-Cap Growth29.8578.5351.0737.8721.7521.121.071.07550.0633.961.471.160.621000000.00.0029
6Baron Focused Growth Fund Retail Shares (BFGFX)Mid-Cap Growth29.5578.0150.6537.5121.4516.531.351.35550.0633.951.461.160.622500.00.0010
7Jacob Internet Investor Class (JAMFX)Technology38.5789.6350.4735.2723.374.382.502.50216.0733.611.471.360.562500.00.0000
8Shelton Green Alpha Fund (NEXTX)Mid-Cap Growth13.3156.3749.7530.02NaN21.121.281.28550.0630.081.621.380.682500.00.0175
9Morgan Stanley Institutional Fund Trust Discovery Portfolio Class I (MPEGX)Mid-Cap Growth8.6733.4049.5540.2320.9515.490.740.74550.0630.891.571.220.705000000.00.0000

Last rows

namemorningstar_categoryytdDailyyr1yr3yr5yr10life_of_fundnet_expense_ratiogross_expense_ratiomorningstar_rating_overallriskstd_devsharpe_ratio_3_yrbetar2minimum_investmentlast_dividend
9616Wells Fargo Special International Small Cap Fund Institutional Class (WICIX)Foreign Small/Mid Blend17.9935.00NaNNaNNaN15.991.055.79NaN7NaNNaNNaNNaN1000000.00.04028
9617Westwood Quality AllCap Fund Institutional Shares (WQAIX)Large Value5.31NaNNaNNaNNaN5.900.651.23NaN6NaNNaNNaNNaN100000.0NaN
9618Westwood SmallCap Growth Fund Institutional Shares (WSCIX)Small Growth8.09NaNNaNNaNNaN2.600.7510.25NaN7NaNNaNNaNNaN100000.0NaN
9619William Blair China Growth Fund Class I (WICGX)China Region1.80NaNNaNNaNNaN2.801.051.25NaN8NaNNaNNaNNaN500000.0NaN
9620William Blair Emerging Markets Debt Fund Class I (WEDIX)Emerging Markets Bond0.50NaNNaNNaNNaN0.190.700.90NaN5NaNNaNNaNNaN500000.00.03953
9621William Blair Small-Mid Cap Core Fund Class I (WBCIX)Small Blend31.2956.96NaNNaNNaN26.960.951.22NaN7NaNNaNNaNNaN500000.00.00658
9622X-Square Balanced Fund, LLC Class A (SQBFX)Allocation--50% to 70% EquityNaN17.56NaNNaNNaN12.212.7613.08NaN5NaNNaNNaNNaN5000.00.12000
9623X-Square Balanced Fund, LLC Class C (SQCBX)Allocation--50% to 70% EquityNaN16.65NaNNaNNaN11.363.5114.82NaN5NaNNaNNaNNaN5000.00.12000
9624X-Square Balanced Fund, LLC Institutional Class (SQBIX)Allocation--50% to 70% EquityNaN17.79NaNNaNNaN12.482.5112.01NaN5NaNNaNNaNNaN100000.00.12000
9625Zeo Sustainable Credit Fund Class I (ZSRIX)Multisector Bond4.937.84NaNNaNNaN2.170.991.66NaN4NaNNaNNaNNaN5000.00.05280